Principal component analysis for accelerating color bilateral filtering

Abstract

In this paper, we propose to speed up bilateral filtering by principal component analysis (PCA)-based dimensionality compression method with constant-time bilateral filtering. Constant-time bilateral filtering speeds up the filtering by representing it as a summation of the multiple Gaussian filters. However, a simple implementation is of the order of O(K 3 ) for color and suffers from the curse of dimensionality. A clustering-based approximation speedup solves this problem with an order of O(K) or O(K 2 ). PCA can provide a more informative signal relative to the transformation matrix. We have accelerated the process by converting the color information of the input image from 3-channel to 1-channel by PCA, considering the constant-time bilateral filter as joint bilateral filtering, and using the transformed image as a guide image. This dimensional reduction allowed us to filter images with sufficient accuracy at a higher speed.

Decomposed Multilateral Filtering for Accelerating Filtering with Multiple Guidance Images

“…In Section 7.1 , guide images are grayscaled 2-channel, but here, we use two RGB images, 6-channel. In addition, the number of channels is controlled by using PCA dimensionality compression for guide images [ 37 , 89 ]. Note that the denoising performance is different from the approximation performance.…”